Monitoring system for three-dimensional printing

ABSTRACT

An inference system for monitoring a cementitious mixture for three-dimensional printing is provided. The inference system includes an ambient condition sensor, a temperature sensor, a moisture sensor and an image capturing device. The inference system also includes a controller coupled to the ambient condition sensor, the temperature sensor, the moisture sensor, and the image capturing device. The controller receives sensed ambient conditions, a temperature signal, and a moisture content signal. The controller receives an image feed of a portion of a cementitious mixture. The controller also receives signals indicative of a motor speed and a motor torque associated with a mixing container. The controller builds a model and determines a material suitability of the cementitious mixture using the model based on the received ambient conditions, the temperature signal, the moisture content signal, the image feed, the motor speed, and the motor torque and determines one or more corrective actions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and is a continuation of U.S. patentapplication Ser. No. 15/730,071, filed Oct. 11, 2017, entitled“Monitoring System For Three-Dimensional Printing,” the disclosure ofwhich is incorporated by reference herein in its entirety for allpurposes.

TECHNICAL FIELD

The present disclosure relates to a monitoring system, and morespecifically, to a system and method for monitoring a cementitiousmixture for three-dimensional printing.

BACKGROUND

Additive construction applies three-dimensional printing technology at alarge scale, depositing construction material layer upon layer, toconstruct suitable buildings and structures in a faster and lesslabor-intensive way. Before and during the construction printingprocess, a cementitious mixture needs to be prepared. The cementitiousmixture used in such applications has a very high and constrainedstandard compared to that of any other regular cementitious mix due tothe specificity of the application.

For example, viscosity of the cementitious mixture is one suchparameter. The material properties of the cementitious mixture forthree-dimensional printing cannot be too dry to flow and cannot be toowet to keep the shape or sustain the next layer. There may be othermaterial properties that may need to be met as well. Besides that, thecementitious mixture may be highly influenced by the environment, suchas ambient temperature, moisture, and the quality of raw mixingmaterials. However, a pre-determined ratio of specific components maynot always result in the formation of the ideal cementitious mixture.

Knowledgeable personnel may be required to be present during the mixingprocedure to check the quality of the cementitious mixture. Sometimes,the personnel may need to add some components, such as sand or water, toimprove the material properties of the cementitious mixture. This may bea laborious and time-consuming process which is susceptible to humanerrors. Further, the mixing procedure may rely on knowledge, domainexpertise, and intuitiveness of the personnel, making it challenging fornovice personnel to accurately perform such tasks.

United States Published Application Number 2014/0252668 describes anapparatus for performing a multi-layer construction method usingcementitious material. The apparatus has a reservoir for containingcementitious material. The reservoir is coupled to a print head with adelivery nozzle. The delivery nozzle can be moved by a robotic armassembly to index the nozzle along a predetermined path. Flow of thecementitious material from the reservoir to the nozzle and to extrudethe material out of the nozzle is controlled in conjunction withindexing of the nozzle. A support material, an accelerating agent and acartilage material may also be deposited from the print head.

SUMMARY OF THE DISCLOSURE

In one aspect of the present disclosure, an inference system formonitoring a cementitious mixture for three-dimensional printing isprovided. The inference system includes an ambient condition sensorconfigured to sense ambient conditions associated with a mixingcontainer. The inference system includes a temperature sensor configuredto generate a temperature signal of the cementitious mixture. Theinference system includes a moisture sensor configured to generate amoisture content signal associated with the cementitious mixture. Theinference system includes an image capturing device configured togenerate an image feed of at least a portion of the cementitious mixturewithin the mixing container. The inference system includes a controllercoupled to the ambient condition sensor, the temperature sensor, themoisture sensor, and the image capturing device. The controller isconfigured to receive the sensed ambient conditions. The controller isconfigured to receive the temperature signal. The controller isconfigured to receive the moisture content signal. The controller isconfigured to receive the image feed of the portion of the cementitiousmixture. The controller is configured to receive signals indicative of amotor speed and a motor torque associated with the mixing container. Thecontroller is configured to build a model and determine a materialsuitability of the cementitious mixture using the model based on thereceived ambient conditions, the temperature signal, the moisturecontent signal, the image feed, the motor speed, and the motor torque.The controller is configured to determine one or more corrective actionsbased on the determined material suitability.

In another aspect of the present disclosure, a method for monitoring acementitious mixture for three-dimensional printing is provided. Themethod includes receiving, by a controller, sensed ambient conditionsassociated with a mixing container from an ambient condition sensor. Themethod includes receiving, by the controller, a temperature signal ofthe cementitious mixture from a temperature sensor. The method includesreceiving, by the controller, a moisture content signal associated withthe cementitious mixture from a moisture sensor. The method includesreceiving, by the controller, an image feed of at least a portion of thecementitious mixture within the mixing container from an image capturingdevice. The method includes receiving, by the controller, signalsindicative of a motor speed and a motor torque associated with themixing container. The method includes building a model and determining,by the controller, a material suitability of the cementitious mixtureusing the model based on the received ambient conditions, thetemperature signal, the moisture content signal, the image feed, themotor speed, and the motor torque. The method includes determining, bythe controller, one or more corrective actions based on the determinedmaterial suitability.

In yet another aspect of the present disclosure, a printing assembly forthree-dimensional printing using a cementitious mixture is provided. Theprinting assembly includes a pump, a mixing container coupled to thepump, and an inference system for the mixing container. The inferencesystem includes an ambient condition sensor configured to sense ambientconditions associated with a mixing container. The inference systemincludes a temperature sensor configured to generate a temperaturesignal of the cementitious mixture. The inference system includes amoisture sensor configured to generate a moisture content signalassociated with the cementitious mixture. The inference system includesan image capturing device configured to generate an image feed of atleast a portion of the cementitious mixture within the mixing container.The inference system includes a controller coupled to the ambientcondition sensor, the temperature sensor, the moisture sensor, and theimage capturing device. The controller is configured to receive thesensed ambient conditions. The controller is configured to receive thetemperature signal. The controller is configured to receive the moisturecontent signal. The controller is configured to receive the image feedof the portion of the cementitious mixture. The controller is configuredto receive signals indicative of a motor speed and a motor torqueassociated with the mixing container. The controller is configured tobuild a model and determine a material suitability of the cementitiousmixture using the model based on the received ambient conditions, thetemperature signal, the moisture content signal, the image feed, themotor speed, and the motor torque. The controller is configured todetermine one or more corrective actions based on the determinedmaterial suitability.

Other features and aspects of this disclosure will be apparent from thefollowing description and the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a perspective view of an exemplary machine having a mixingcontainer, in accordance with the concepts of the present disclosure;

FIG. 2 is a perspective view of the mixing container, in accordance withthe concepts of the present disclosure;

FIG. 3 is a block diagram of an inference system associated with themixing container, in accordance with the concepts of the presentdisclosure; and

FIG. 4 is a flowchart of a method for monitoring a cementitious mixturefor three-dimensional printing, in accordance with the concepts of thepresent disclosure.

DETAILED DESCRIPTION

The present disclosure relates to a scoop and mix system forthree-dimensional additive manufacturing of objects using a cementitiousmixture. Referring to FIG. 1 , the scoop and mix system includes anexemplary machine 100. The machine 100 is embodied as a skid steerloader. Alternatively, any other known set-up having a mixing containerthat is suitable for the preparation of the cementitious mixture foradditive manufacturing may be utilized without deviating from the scopeof the present disclosure.

The machine 100 includes an operator cab 102 supported on a frame 104 ofthe machine 100. A pair of lift arms 106 are pivotably attached to theframe 104 and extend longitudinally on both sides of the operator cab102. The lift arms 106 attach at pivot points behind the operator cab102 of the machine 100 and supports a bucket 108. In the presentdisclosure, the bucket 108 acts as the mixing container 110 in whichdifferent components, such as sand, aggregate, cement, water, and otheradditives, are introduced for forming the cementitious mixture.

The machine 100 is propelled by tracks 112. The machine 10 also includesa rear mounted engine compartment 114 supported on the frame 104. Theoperator cab 102 of the machine 100 houses controls for controlling amovement of the machine 100 on ground. The machine 100 also includesother components that are not described here to maintain simplicity andease of understanding.

Referring to FIG. 2 , an enlarged view of the bucket 108, hereinafterreferred to as the mixing container 110 is illustrated. The depth anddimensions of the mixing container 110 are such that the ingredients,raw materials or components for making the cement mixture can bereceived into the mixing container 110. During operation, an operatormay manually introduce a variety of the components into an opening 202of the mixing container 110. In other embodiments, an automated systemmay introduce the components used to form the cement mixture into themixing container 110 with little or no human intervention.

The mixing container 110 includes a protective grate 204 at the opening202 of the mixing container 110. The grate 204 may also include teeth206 for breaking the components into smaller pieces and/or for rippingopen bags of the components. A hydraulically driven impeller (not shown)is connected to an auger (not shown) present within the bucket 108. Theoperator seated in the operator cab 102 may use the controls providedwithin the operator cab 102 to operate an auxiliary pump 310 (see FIG. 3) associated with the impeller. During a mixing operation, the pump 310may thus drive the impeller, causing the components present in themixing container 110 to be mixed for forming the cementitious mixture. Aperson of ordinary skill in the art will appreciate that although thepresent disclosure describes the scoop and mix system for forming thecementitious mixture, any other known system may also be utilized. Afterthe mixing operation is complete, the cementitious mixture is made toexit the bucket 108 through a door (not shown) and is introduced to acementitious mixture delivery pump (not shown).

The present disclosure relates to an inference system 300 (see FIG. 3 )for real-time monitoring of the cementitious mixture being formed withinthe mixing container 110. In one example, the cementitious mixture maybe a concrete mixture. The inference system 300 is an automated andintelligent system for monitoring a material suitability of thecementitious mixture as it is being mixed and formed. The materialsuitability includes predefined material properties of the cementitiousmixture including stiffness, flowability, shear strength, cohesion, andslump of the cementitious mixture to ensure that the cementitiousmixture being formed is consistent with the constrained requirements forthree-dimensional printing applications and is five from materialvariations.

More particularly, the inference system 300 estimates the materialproperties and suitability of the cementitious mixture being mixedwithin the mixing container 110 and may even provide suggestivecorrective actions to overcome deficiencies that may be estimated by thesystem. The components and working of the inference system 300 will nowbe described in detail.

Referring to FIG. 3 , the inference system 300 includes an ambientcondition sensor 302. The ambient condition sensor 302 may be mounted atany suitable location on the machine 100. The ambient condition sensor302 is configured to sense ambient conditions associated with the mixingcontainer 110. More specifically, the ambient condition sensor 302provides signals indicative of ambient temperature and humidity levelsin an area surrounding the mixing container 110.

The inference system 300 also includes a number of sensors mountedwithin or at the opening 202 of the mixing container 110 for measuringdifferent parameters of the cementitious mixture. The inference system300 includes a temperature sensor 304 and a moisture content sensor 306.The temperature sensor 304 and the moisture content sensor 306 may besurface mounted sensors within the mixing container 110, such that thetemperature sensor 304 and the moisture content sensor 306 may come incontact with the cementitious mixture as it is being mixed within themixing container 110. The temperature sensor 304 is configured togenerate a temperature signal indicative of a temperature of thecementitious mixture. The moisture content sensor 306 is configured togenerate a moisture content signal indicative of a moisture content ofthe cementitious mixture. It should be noted that the temperature andmoisture content sensors 304, 306 may monitor and provide real-time dataof respective parameters of the cementitious mixture as the mixture isbeing mixed within the mixing container 110.

The inference system 300 further includes an image capturing device 308.The image capturing device 308 includes a camera, a camcorder, or anyother known image or video capturing device. The image capturing device308 is mounted on the frame of the machine 100 and is positioned anddirected to capture an image feed of at least a portion of thecementitious mixture. That is, the image capturing device 308 isoriented to face the opening 202 of the mixing container 110, so thatthe image capturing device 308 is directed towards an inner portion ofthe mixing container 110 and the cementitious mixture being mixed fallswithin a field of view of the image capturing device 308. Accordingly,the image capturing device 308 captures the image feed of thecementitious mixture as the mixture is being mixed within the mixingcontainer 110. More particularly, the image feed provided by the imagecapturing device 308 may be used by the inference system 300 to estimateand measure a variety of parameters of the cementitious mixtureincluding for example, flowability and moisture content of thecementitious mixture, and will be explained in detail later in thissection.

The inference system 300 also includes sensors associated with theauxiliary pump 310 associated with the mixing container 110. Forexample, the inference system 300 is configured to measure a supply sidepressure to the pump 310. Since a volumetric displacement of the pump310 is known, the system is configured to determine a motor torqueoutput. Further, the system may include a Hall effect sensor associatedwith the impeller. This may be used by the system to determine the motorspeed. A person of ordinary skill in the art will appreciate that otherknown methods may be used to determine the motor torque and the motorspeed of the system without deviating from the scope of the presentdisclosure, for example, this data may be received from a small paddlewheel in a situation when speed or torque of the impeller cannot bedirectly measured.

A controller 312 is coupled to the ambient condition sensor 302, thetemperature sensor 304, the moisture content sensor 306, and the pump310. The controller 312 is configured to receive the sensed ambientconditions from the ambient condition sensor 302. More particularly, thecontroller 312 receives the ambient temperature data and the humiditydata from the ambient condition sensor 302. The controller 312 alsoreceives the temperature signal from the temperature sensor 304indicative of the temperature of the cementitious mixture. Thecontroller 312 receives the moisture content signal from the moisturecontent sensor 306 indicative of the moisture content of thecementitious mixture. The controller 312 further receives the image feedof the cementitious mixture from the image capturing device 308.

The controller 312 performs image analysis on the image feed receivedfrom the image capturing device 308. The controller 312 may utilizeknown computer vision and object detection algorithms on multiple framesof the image feed to estimate in time how the cementitious mixtureshifts and distorts around the impeller within the mixing container 110.Accordingly, the controller 312 may estimate the flowability of thecementitious mixture. Further, the controller 312 may analyze themoisture content, the color, the texture and other visual informationobtained from the image feed. For example, if the color of thecementitious mixture is relatively dark, the controller 312 may estimatethat the moisture content of the cementitious mixture is high.

Also, the controller 312 receives the data related to the motor torqueand the motor speed of the pump 310. This may be used by the controller312 to estimate a shear resistance of the cementitious mixture. Based onthe received data, that is, the sensed ambient conditions, thetemperature and the moisture content of the cementitious mixture, theimage feed, the motor torque and the motor speed, the controller 312builds a model and determines the material suitability of thecementitious mixture using the model. Further, the controller 312 maydetermine any deficiencies that exist in the material suitability andsuggest one or more corrective actions to be performed by the operatorto overcome the deficiencies so that the material properties of thecementitious mixture may be corrected as desired. For example, if theambient temperature conditions are too hot or dry, the moisture contentof the cementitious mixture may be affected requiring suitablecorrective actions to be taken.

A predictive regression or classification model implemented by thecontroller 312 may be used to determine the material suitability of thecementitious mixture. The regression model may infer material propertiessuch as shear strength, material cohesion, viscosity, etc. and comparethese with respective acceptable material property ranges to see if thematerial properties of the cementitious mixture lies within this definedregion of suitability. Alternatively, the classification model maysimply predict whether the cementitious mixture is suitable ornon-suitable (for example, a binary classifier). A person of ordinaryskill in the art will appreciate that either of these models may bebuilt using sufficient training data and an appropriate algorithm chosenby one skilled in the art, such as, but not limited to, random forest,kernel estimate, and recurrent neural network.

It should be noted that if image/video inputs are used in the predictivemodel, then the system is likely to use a deep neural network, such asthe convolutional network, to deal with the high-dimensionality of theimages/videos. An advanced algorithm which combines various neuralnetwork architectures (RNNs, CNNs, MLPs, etc.) could also be implementedwith no loss of generality. In the present disclosure, the term“building the mode” means getting enough recorded training data torepresent the variable distribution of inputs relative to a measured,ground truth set of material properties of the cementitious mixture.This data is then run through the algorithm which will learn the modelparameters necessary to predict the material properties accurately onthe held-out data. This process is known in the art as predictive modeltraining and inference and is well known in the field of artificialintelligence, machine learning, and deep learning in order of increasedspecificity.

In this case, the controller 312 includes a general deep neural networkthat estimates a consensus of all the parameters that are measured bythe system in the final estimation of the material properties of thecementitious mixture and identifies any deficiencies that may exist. Thedeep neural network may be a convolutional or recurrent neural network.A person of ordinary skill in the art will appreciate that deep neuralnetworks can model complex non-linear relationships.

The deep neural network architectures generate compositional modelswhere the material suitability of the cementitious mixture is expressedas a layered composition of primitives based on the input parameters,for example, the ambient conditions, the temperature and moisturecontent of the cementitious mixture, the motor torque and speed, and theimages of the cementitious mixture. The system has a logic based ruleset for evaluating the material suitability of the cementitious mixturethrough regression. The deep neural network includes a large network ofweights and biases associated with the input parameters, like that in anempirical formula relating the inputs, such that these inputs convergeto allow the controller 312 to approximate the material suitability ofthe cementitious mixture. Initially the system may accept a ground truthin which the material properties of the cementitious mixture is testedusing traditional or known Rheology testing methods so that the systemmay regress onto the given parameters.

By iterative training of the system, the ground truth may be eliminatedin later stages of development, and the system may provide real-timematerial estimation of the material suitability of the cementitiousmixture as it is being mixed in the mixing container 110. The controller312 accepts multimodal data that are of different types, includingvisual data, temperature data, torque and speed data, and ambientcondition data. Accordingly, a suitable deep neural network may beselected. For example, a branched deep neural network having severalfully connected convolution layers to process temperature data, and themotor torque and speed data may be utilized. Further, other temporaldata may be processed through recurrent layers such that the outputloops back to the long short-term memory (LSTM) to maintain the memoryof states.

The controller 312 determines the material suitability of thecementitious mixture in real-time based on the input parameters. Aperson of ordinary skill in the art will appreciate that the materialrequirements are constrained for cementitious mixtures used in additivemanufacturing. Hence, if the material suitability determined by thesystem is not as expected, the controller 312 may additionally oroptionally provide one or more corrective actions through which theoperator may restore the material suitability of the cementitiousmixture within acceptable limits. The controller 312 provides guidanceon how to improve deficient mix conditions based on the evaluation ofthe material suitability of the cementitious mixture. The controller 312identifies deficiencies in one or more of the input parameters andintelligently provides corrective actions to change the materialsuitability of the cementitious mixture. For example, if the controller312 evaluates that the cementitious mixture is too wet, the controller312 may appropriately suggest one or more corrective actions, such as,adding a dry component, for example sand, to the cementitious mixtureand/or waiting for some time to elapse before proceeding with furthermixing of the cementitious mixture.

The controller 312 is also coupled to a display unit 314. The displayunit 314 may include a monitor, a screen, a touchscreen, or any othervisual and/or auditory output unit. In one example, the display unit 314is positioned at such a location that the operator who introduces thecomponents into the mixing container 110 may easily view the displayunit 314. The controller 312 may notify the operator of the determinedmaterial suitability of the cementitious mixture to the operator via thedisplay unit 314. Further, the controller 312 may also notify theoperator of the one or more corrective actions, if required, through thedisplay unit 314. Based on the requirements, other interim results thatare monitored or evaluated by the controller 312 may also be displayedto the operator through the display unit 314.

As described earlier, the controller 312 monitors and computes thematerial suitability of the cementitious mixture in real-time from astart of the mixing operation until the cementitious mixture is preparedand/or corrected. The controller 312 may be located at any suitablelocation either on or off-board the machine 100. The cementitiousmixture described herein may be used to create any suitable structuralobject. Once the cementitious mixture is prepared in the bucket 108, thecementitious mixture is transferred to the cementitious delivery pump

In other examples, the present disclosure is also applicable to a hopperassociated with a pumping system that is connected to an extrusionnozzle. It should be noted that once the cementitious mixture isdeposited from a mixing system into the pumping system, the cementitiousmixture may still need to be monitored so that deficiencies in thematerial properties may be restored. In some cases, the working of thecontroller 312 may be a factor in the system coordinating a balancebetween pump flow and linear rate of the extrusion nozzle.

INDUSTRIAL APPLICABILITY

The present disclosure relates to the system 300 and method 400 forinferring and estimating material suitability of the cementitiousmixture used in fused deposition modelling or additive manufacturing.Referring to FIG. 4 , at step 402, the controller 312 receives thesensed ambient conditions associated with the mixing container 110 fromthe ambient condition sensor 302. At step 404, the controller 312receives the temperature signal of the cementitious mixture from thetemperature sensor 304. At step 406, the controller 312 receives themoisture content signal associated with the cementitious mixture fromthe moisture content sensor 306.

At step 408, the controller 312 receives the image feed of at least theportion of the cementitious mixture within the mixing container 110 fromthe image capturing device 308. At step 410, the controller 312 receivesthe signals indicative of the motor speed and the motor torqueassociated with the mixing container 110. At step 412, the controller312 builds a model and determines the material suitability of thecementitious mixture using the model based on the received ambientconditions, the temperature signal, the moisture content signal, theimage feed, the motor speed, and the motor torque. At step 414, thecontroller 312 determines one or more corrective actions based on thedetermined material suitability.

The system of the present disclosure provides a robust solution foreffectively and dynamically evaluating the material suitability of thecementitious mixture and suggesting corrective actions to improve thematerial suitability, if required. The system makes use of inputs from arobust sensor suite associated with the mixing container 110 that caneasily be deployed. The system reduces reliance on expertise of theoperator who is performing the mixing operation, and provides anaccurate estimation of the material properties through real-timeevaluation. Good material suitability of the cementitious mixture mayyield good material deposition, improving structural stability of anyobject formed using this cementitious mixture.

While aspects of the present disclosure have been particularly shown anddescribed with reference to the embodiments above, it will be understoodby those skilled in the art that various additional embodiments may becontemplated by the modification of the disclosed machines, systems andmethods without departing from the spirit and scope of what isdisclosed. Such embodiments should be understood to fall within thescope of the present disclosure as determined based upon the claims andany equivalents thereof.

What is claimed is:
 1. A method for monitoring a cementitious mixture for three-dimensional printing, the method comprising: receiving, by a controller, an ambient conditions associated with a mixing container from an ambient condition sensor; receiving, by the controller, a temperature signal of the cementitious mixture from a temperature sensor; receiving, by the controller, a moisture content signal associated with the cementitious mixture from a moisture sensor; receiving, by the controller and from a camera directed toward an inner portion of the mixing container, an image feed, wherein the camera is configured to capture the image feed of at least a portion of the cementitious mixture within the mixing container, to determine a material suitability of the cementitious mixture; receiving, by the controller, signals indicative of a motor speed and a motor torque associated with the mixing container; executing a model by the controller, the model configured to receive as inputs the ambient conditions, the temperature signal, the moisture content signal, the image feed, the motor speed, and the motor torque; determining, by the controller, a material suitability of the cementitious mixture, based at least in part on an output of the model; and determining and outputting, by the controller, one or more corrective actions based at least in part on the material suitability of the cementitious mixture.
 2. The method of claim 1, wherein outputting the corrective actions comprises providing, by the controller, a notification of the one or more corrective actions through a display unit.
 3. The method of claim 1, further comprising determining, by the controller, a color of the cementitious mixture based at least in part on the image feed.
 4. The method of claim 1, further comprising determining, by the controller, a texture of the cementitious mixture.
 5. The method of claim 1, further comprising: determining, by the controller, a start of a mixing operation by the mixing container; and initiating real-time monitoring, by the controller, of the ambient conditions, the temperature signal, the moisture content signal, the image feed, the motor speed, and the motor torque, based on the start of the mixing operation.
 6. The method of claim 1, wherein receiving the ambient condition includes: receiving ambient temperature data of an area surrounding the mixing container; and receiving humidity data of the area surrounding the mixing container.
 7. The method of claim 1, wherein determining the material suitability of the cementitious mixture comprises: receiving, as an output from model, at least one inferred material property of the cementitious mixture; and comparing the at least one inferred material property to at least one range of suitable values associated with the at least one inferred material property.
 8. The method of claim 7, wherein the at least one inferred material property includes at least one of: shear strength; stiffness; flowability; slump; material cohesion; or viscosity.
 9. The method of claim 1, wherein the model includes at least one of: a regression model; or a classification model.
 10. A system comprising a controller, the system configured to perform operations comprising: receiving, by the controller, an ambient condition associated with a mixing container from an ambient condition sensor; receiving, by the controller, a temperature signal associated with a cementitious mixture from a temperature sensor; receiving, by the controller, a moisture content signal associated with the cementitious mixture from a moisture sensor; receiving, by the controller and from a camera directed toward an inner portion of the mixing container, an image feed, wherein the camera is configured to capture the image feed of at least a portion of the cementitious mixture within the mixing container, to determine a material suitability of the cementitious mixture; receiving, by the controller, signals indicative of a motor speed and a motor torque associated with the mixing container; executing a model by the controller, the model configured to receive as inputs the ambient condition, the temperature signal, the moisture content signal, the image feed, the motor speed, and the motor torque; determining, by the controller, a material suitability of the cementitious mixture, based at least in part on an output of the model; and determining and outputting, by the controller, one or more corrective actions based at least in part on the material suitability of the cementitious mixture.
 11. The system of claim 10, wherein outputting the corrective actions comprises providing, by the controller, a notification of the one or more corrective actions through a display unit.
 12. The system of claim 10, the operations further comprising determining, by the controller, a color of the cementitious mixture based at least in part on the image feed.
 13. The system of claim 10, the operations further comprising determining, by the controller, a texture of the cementitious mixture.
 14. The system of claim 10, the operations further comprising: determining, by the controller, a start of a mixing operation by the mixing container; and initiating real-time monitoring, by the controller, of the ambient conditions, the temperature signal, the moisture content signal, the image feed, the motor speed, and the motor torque, based on the start of the mixing operation.
 15. The system of claim 10, wherein receiving the ambient conditions includes: receiving ambient temperature data of an area surrounding the mixing container; and receiving humidity data of the area surrounding the mixing container.
 16. The system of claim 10, wherein determining the material suitability of the cementitious mixture comprises: receiving, as an output from model, at least one inferred material property of the cementitious mixture; and comparing the at least one inferred material property to at least one range of suitable values associated with the at least one inferred material property.
 17. The system of claim 16, wherein the at least one inferred material property includes at least one of: shear strength; stiffness; flowability; slump; material cohesion; or viscosity.
 18. The system of claim 10, wherein the model includes at least one of: a regression model; or a classification model. 